"""simple docstring"""



from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


SCREAMING_SNAKE_CASE__:Any									=			"""\
@article{wang2019superglue,
  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
  journal={arXiv preprint arXiv:1905.00537},
  year={2019}
}
"""

SCREAMING_SNAKE_CASE__:List[Any]									=			"""\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
"""

SCREAMING_SNAKE_CASE__:Dict									=			"""
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
    predictions: list of predictions to score. Depending on the SuperGlUE subset:
        - for 'record': list of question-answer dictionaries with the following keys:
            - 'idx': index of the question as specified by the dataset
            - 'prediction_text': the predicted answer text
        - for 'multirc': list of question-answer dictionaries with the following keys:
            - 'idx': index of the question-answer pair as specified by the dataset
            - 'prediction': the predicted answer label
        - otherwise: list of predicted labels
    references: list of reference labels. Depending on the SuperGLUE subset:
        - for 'record': list of question-answers dictionaries with the following keys:
            - 'idx': index of the question as specified by the dataset
            - 'answers': list of possible answers
        - otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
    - for 'record':
        - 'exact_match': Exact match between answer and gold answer
        - 'f1': F1 score
    - for 'multirc':
        - 'exact_match': Exact match between answer and gold answer
        - 'f1_m': Per-question macro-F1 score
        - 'f1_a': Average F1 score over all answers
    - for 'axb':
        'matthews_correlation': Matthew Correlation
    - for 'cb':
        - 'accuracy': Accuracy
        - 'f1': F1 score
    - for all others:
        - 'accuracy': Accuracy
Examples:

    >>> super_glue_metric = datasets.load_metric('super_glue', 'copa')  # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'record')
    >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]
    >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 1.0, 'f1': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')
    >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""



def 	_lowerCamelCase(	a   ,    a				):
       return float((preds == labels).mean()				)



def 	_lowerCamelCase(	a   ,    a   ,    a="binary"				):
       __a       =    simple_accuracy(a   ,    a				)
       __a       =    float(fa_score(y_true=a   ,    y_pred=a   ,    average=a				)				)
       return {
           "accuracy": acc,
           "f1": fa,
       }



def 	_lowerCamelCase(	a   ,    a				):
       __a       =    {}
       for id_pred, label in zip(a   ,    a				):
              __a       =    F"{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"
              __a       =    id_pred["prediction"]
              if question_id in question_map:
                     question_map[question_id].append((pred, label)				)
              else:
                     __a       =    [(pred, label)]
       __a ,   __a       =    [], []
       for question, preds_labels in question_map.items():
              __a ,   __a       =    zip(*a				)
              __a       =    fa_score(y_true=a   ,    y_pred=a   ,    average="macro"				)
              fas.append(a				)
              __a       =    int(sum(pred == label for pred, label in preds_labels				) == len(a				)				)
              ems.append(a				)
       __a       =    float(sum(a				) / len(a				)				)
       __a       =    sum(a				) / len(a				)
       __a       =    float(fa_score(y_true=a   ,    y_pred=[id_pred["prediction"] for id_pred in ids_preds]				)				)
       return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,						_KWARGS_DESCRIPTION       )
class  snake_case__      (       datasets.Metric       ):


 def 						a__   ( self						):
        if self.config_name not in [
            "boolq",
            "cb",
            "copa",
            "multirc",
            "record",
            "rte",
            "wic",
            "wsc",
            "wsc.fixed",
            "axb",
            "axg",
        ]:
               raise KeyError(
                   "You should supply a configuration name selected in "
                   "[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"						)
        return datasets.MetricInfo(
            description=_DESCRIPTION    ,		citation=_CITATION    ,		inputs_description=_KWARGS_DESCRIPTION    ,		features=datasets.Features(self._get_feature_types()						)    ,		codebase_urls=[]    ,		reference_urls=[]    ,		format="numpy" if not self.config_name == "record" and not self.config_name == "multirc" else None    ,		)


 def 						a__   ( self						):
        if self.config_name == "record":
               return {
                   "predictions": {
                       "idx": {
                           "passage": datasets.Value("int64"						),
                           "query": datasets.Value("int64"						),
                       },
                       "prediction_text": datasets.Value("string"						),
                   },
                   "references": {
                       "idx": {
                           "passage": datasets.Value("int64"						),
                           "query": datasets.Value("int64"						),
                       },
                       "answers": datasets.Sequence(datasets.Value("string"						)						),
                   },
               }
        elif self.config_name == "multirc":
               return {
                   "predictions": {
                       "idx": {
                           "answer": datasets.Value("int64"						),
                           "paragraph": datasets.Value("int64"						),
                           "question": datasets.Value("int64"						),
                       },
                       "prediction": datasets.Value("int64"						),
                   },
                   "references": datasets.Value("int64"						),
               }
        else:
               return {
                   "predictions": datasets.Value("int64"						),
                   "references": datasets.Value("int64"						),
               }






 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase						):
        if self.config_name == "axb":
               return {"matthews_correlation": matthews_corrcoef(lowerCamelCase    ,		lowerCamelCase						)}
        elif self.config_name == "cb":
               return acc_and_fa(lowerCamelCase    ,		lowerCamelCase    ,		fa_avg="macro"						)
        elif self.config_name == "record":
               __a       =    [
                   {
                       "qas": [
                           {"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]}
                           for ref in references
                       ]
                   }
               ]
               __a       =    {pred["idx"]["query"]: pred["prediction_text"] for pred in predictions}
               return evaluate_record(lowerCamelCase    ,		lowerCamelCase						)[0]
        elif self.config_name == "multirc":
               return evaluate_multirc(lowerCamelCase    ,		lowerCamelCase						)
        elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
               return {"accuracy": simple_accuracy(lowerCamelCase    ,		lowerCamelCase						)}
        else:
               raise KeyError(
                   "You should supply a configuration name selected in "
                   "[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"						)

"""simple docstring"""



from __future__ import annotations

from math import pi



def 	_lowerCamelCase(	a   ,    a   ,    a				):

       if (inductance, frequency, reactance).count(0				) != 1:
              raise ValueError("One and only one argument must be 0"				)
       if inductance < 0:
              raise ValueError("Inductance cannot be negative"				)
       if frequency < 0:
              raise ValueError("Frequency cannot be negative"				)
       if reactance < 0:
              raise ValueError("Inductive reactance cannot be negative"				)
       if inductance == 0:
              return {"inductance": reactance / (2 * pi * frequency)}
       elif frequency == 0:
              return {"frequency": reactance / (2 * pi * inductance)}
       elif reactance == 0:
              return {"reactance": 2 * pi * frequency * inductance}
       else:
              raise ValueError("Exactly one argument must be 0"				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
